Determination of Enhancing Structures in an Anatomical Body Part

ABSTRACT

A data processing method for determining an enhancing structure of interest within an anatomical body part, wherein the structure of interest exhibits an enhanced signal in an image of the anatomical body part generated by a medical imaging method using a contrast agent, said method being designed to be performed by a computer and comprising a region growing algorithm.

The present invention relates to the determination of enhancingstructures (referred to as “enhancing structures of interest” or“structures of interest” for short, which are in particular tumours,metastases, etc.) within an anatomical body part. The determination isbased on images acquired by a medical imaging method using a contrastagent.

The terms used in the application are defined in a separate sectionentitled “Definitions”.

The structures of interest as mentioned above are in particularstructures which accumulate the contrast agent. An example of such astructure of interest is in particular a tumour. Accordingly, “tumour”is also used, merely by way of example, for “structure of interest” inthe following. Other examples of structures of interest include ametastasis and a nidus of malformations in arterial or venousstructures.

With respect to the technical background of the invention, reference ismade in particular to the following paper:

Kanaly, C. et al.: “A Novel Method for Volumetric MRI ResponseAssessment of Enhancing Brain Tumor” in PLOS ONE (an online publication)6(1), 2011.

The abstract of the aforementioned paper reads as follows:

“Current radiographic response criteria for brain tumors have difficultydescribing changes surrounding postoperative resection cavities.Volumetric techniques may offer improved assessment, however usually aretime-consuming, subjective and require expert opinion and specializedmagnetic resonance imaging (MRI) sequences. We describe the applicationof a novel volumetric software algorithm that is nearly fully automatedand uses standard T1 pre- and post-contrast MRI sequences. T1-weightedpre- and post-contrast images are automatically fused and normalized.The tumor region of interest is grossly outlined by the user. An atlasof the nasal mucosa is automatically detected and used to normalizelevels of enhancement. The volume of enhancing tumor is thenautomatically calculated. We tested the ability of our method tocalculate enhancing tumor volume with resection cavity collapse and whenthe enhancing tumor is obscured by subacute blood in a resection cavity.To determine variability in results, we compared narrowly-defined tumorregions with tumor regions that include adjacent meningeal enhancementand also compared different contrast enhancement threshold levels usedfor the automatic calculation of enhancing tumor volume. Our methodquantified enhancing tumor volume despite resection cavity collapse. Itdetected tumor volume increase in the midst of blood products thatincorrectly caused decreased measurements by other techniques. Similartrends in volume changes across scans were seen with inclusion orexclusion of meningeal enhancement and despite different automatedthresholds for tissue enhancement. Our approach appears to overcome manyof the challenges with response assessment of enhancing brain tumors andwarrants further examination and validation.”

The object of the present invention is to provide a more robustdetermination of the structure of interest, wherein the presentinvention in particular allows the influence of the user on the finaldetermination of the structure of interest to be reduced. Determiningthe structure of interest in particular involves determining spatialinformation of the structure of interest, in particular its geometryand/or position. The present invention in particular enables thestructure of interest to be automatically determined.

Further background information, in particular with respect to thetechnique (which can be used to implement the present invention), isdescribed in the following documents:

U.S. Pat. No. 8,280,133 B2, being the document “Method and System forBrain Tumor Segmentation in 3D Magnetic Resonance Images” by Wels et al.

U.S. Pat. No. 7,995,825 B2, being the document “Histogram Segmentationof FLAIR Images” by Jack et al.

US 2007/0064983 A1, being the document “Method for AutomaticallyDetecting Nasal Tumor” by Huang et al.

WO 03/009214 A1, being the document “System and Method for QuantifyingTissue Structures and their Change over Time” by Tamez-Pena et al.

Vincent, L.: “Morphological Grayscale Reconstruction in Image Analysis:Applications and Efficient Algorithms” in IEEE Transactions on imageProcessing, Vol. 2, No. 2, pages 176-201, April 1993.

The present invention relates more generally to the field of medicine,in particular to the field of processing medical image data (alsoreferred to as “medical images” or simply “images”) acquired by means ofmedical imaging methods (see definition below). The above-mentionedobject is solved by the subject-matter of any appended independentclaim. Advantages, advantageous features, advantageous embodiments andadvantageous aspects of the present invention are disclosed in thefollowing and contained in the subject-matter of the dependent claims.Different advantageous features can be combined in accordance with theinvention wherever technically expedient and feasible. Specifically, afeature of one embodiment which has the same or a similar function toanother feature of another embodiment can be exchanged with said otherfeature, and a feature of one embodiment which adds an additionalfunction to another embodiment can in particular be added to said otherembodiment.

In the following, advantageous features and advantageous embodiments ofthe present invention are described.

The present invention is in particular directed to a data processingmethod for determining a structure of interest, in particular one ormore structures of interest. The structure of interest is determinedwithin an anatomical body part. “Determining the structure of interestwithin the anatomical body part” in particular means “determining animage of the structure of interest within an image of the anatomicalbody part”, i.e. the present invention is in particular directed to theprocessing of image data acquired by medical imaging methods in order todetermine spatial information concerning the structure of interest. Theterm “spatial information” encompasses the position and/or geometry(shape and/or size) of at least one enhancing structure of interestwithin the anatomical body part, in particular relative to theanatomical body part. The medical image used for determining thestructure of interest is in particular a processed medical image whichis preferably generated by applying the data processing method of thepresent invention to an enhanced image and a non-enhanced image, asdescribed below. The processed medical image is in particular aprocessed binary image as described below, wherein the binary image isprocessed by a region-growing algorithm as described below.

The structure of interest has the property of exhibiting an enhancedsignal in an image of the anatomical body part generated by the medicalimaging method using a contrast agent, i.e. the enhancement of thesignal is caused by the contrast agent accumulated in the structure ofinterest. In this regard, determining a structure of interestencompasses and in particular does not exclude determining otherenhancing structures of interest (spatially distant from the firststructure of interest) within the same anatomical body part inaccordance with the present invention, in particular parallel orsequential to determining the enhancing structure of interest. The term“enhancing structure of interest” is therefore to be interpreted asmeaning “at least one enhancing structure of interest”.

The anatomical body part is a part of a human or animal body. The partsare in particular parts shown in an anatomical atlas and can for examplebe organs or other anatomical parts such as the head.

In accordance with one step of the data processing method, an imagereferred to as an “enhanced image” of the anatomical body part isacquired. Specifically, the enhanced image is acquired by acquiring datawhich describe the enhanced image and which are referred to as “enhancedimage data”. The acquired enhanced image has been obtained by using amedical imaging method and optionally subjecting the image generated bythe medical imaging method to further image processing (see for examplethe application of a spatial filter as described below), in particularin order to suppress structures which are unlikely to be related to thestructure of interest and/or in order to suppress inhomogeneities (seefor example the section on “inhomogeneity correction” below). Themedical imaging method has been performed using the contrast agent, asmentioned above. The contrast agent is in particular used byadministering it to a patient (within whom the anatomical body part issituated), in particular before starting to generate the enhanced imageby means of the medical imaging method. The structure of interestexhibits an enhanced signal in the enhanced image, wherein the term“enhanced” in particular means that the signal intensity deviates fromthe signal intensity obtained when the same medical imaging method isperformed without using the contrast agent. This deviation is inparticular above the average variation exhibited by a non-enhancedsignal in the image.

In accordance with another step of the present invention, which can beperformed before or after the aforementioned step, an image referred toas a “non-enhanced image” is acquired. Specifically, data which arereferred to as “non-enhanced image data” and describe the non-enhancedimage are acquired. The non-enhanced image is obtained by performing amedical imaging method and optionally also further image processing suchas for example the inhomogeneity correction mentioned above anddescribed further below. The medical imaging method is in particular thesame medical imaging method as that mentioned above with respect togenerating the enhanced image, except that the medical imaging methoddoes not use the contrast agent in this case. This means in particularthat the contrast agent is not administered to the patient before thenon-enhanced image is obtained.

In accordance with another step of the data processing method, a spatialcorrelation between the enhanced image and the non-enhanced image isestablished. The enhanced image and non-enhanced image which have beenspatially correlated with respect to each other are referred to as thespatially correlated enhanced image and spatially correlatednon-enhanced image, respectively. The enhanced image and non-enhancedimage are in particular registered with respect to each other. To thisend, a common reference system for the enhanced image and non-enhancedimage is preferably created. Preferably, the images are spatiallycorrelated, in particular registered, by means of image fusion methods(see definition below). In particular, rigid or elastic image fusion isperformed. In this case, a common reference system is established, i.e.sub-regions of the spatially correlated enhanced image and the spatiallycorrelated non-enhanced image which represent the same sub-region of theanatomical body will have a similar and in particular identical positionand/or geometry in the common reference system. These sub-regions arereferred to as “corresponding sub-regions”. Accordingly, voxels of thespatially correlated enhanced image and the spatially correlatednon-enhanced image (and of the normalised difference image, see below)which represent the same part of the anatomical body and have a similarand in particular identical position in the common reference system arereferred to as “corresponding voxels”. Preferably, all the voxels of thespatially correlated enhanced image and the spatially correlatednon-enhanced image which represent the same part of the anatomical bodyhave a similar and in particular identical position in the commonreference system.

In accordance with another step of the data processing method, an imagereferred to as a “normalised difference image” is determined. As theterm “normalised difference image” implies, this image is determined byperforming a normalising operation and a difference operation.Specifically, the intensity distribution of the voxels of the enhancedimage and the intensity distribution of the voxels of the non-enhancedimage are normalised with respect to each other in order to render theintensities comparable. This normalising operation is performed on thebasis of intensities, in particular intensity distributions, withinsub-regions of the enhanced image and non-enhanced image which arecomparable to each other and preferably comprise sub-regions referred toas “non-enhanced sub-regions”.

To this end, the non-enhanced sub-regions are acquired, preferablybefore the normalised difference image is determined. In particular, anon-enhanced sub-region is acquired in the enhanced image, and anothernon-enhanced sub-region is acquired in the non-enhanced image. Thesenon-enhanced sub-regions are preferably sub-regions which do not exhibitan enhanced signal. The sub-regions can be selected by an operator orcan be automatically determined, for example by comparing the enhancedimage with the non-enhanced image with respect to its local intensitydistribution. The non-enhanced sub-region of the enhanced imagepreferably has the same position and/or geometry as the non-enhancedsub-region of the non-enhanced image in a common reference systemobtained in the spatial correlation step, i.e. the non-enhancedsub-region of the enhanced image and the non-enhanced sub-region of theenhanced image are corresponding sub-regions. The sub-region can bedetermined either by a user or automatically. If automaticallydetermined, the intensity distribution in the enhanced image is inparticular analysed in order to identify a sub-region with a standarddeviation in its intensity and/or an average intensity which is lowerthan the average standard deviation and/or in order to identify asub-region with an average intensity which is lower than the averageintensity in a plurality of sub-regions of the enhanced image.Alternatively or additionally, symmetry properties of the anatomicalbody parts can be used to automatically determine the non-enhancedsub-region, as explained below.

The normalising operation can be performed by adjusting the intensitydistribution within the non-enhanced sub-region of the enhanced imagewith respect to the intensity distribution within the non-enhancedsub-region of the non-enhanced image. This can in particular be achievedby adjusting the intensity distribution in the (spatially correlated)enhanced and/or non-enhanced image such that the average intensity inthe respective (corresponding) non-enhanced sub-regions is made equal.This can for example be achieved by adding or subtracting a constantintensity value from the intensities of (all) the voxels of the(spatially correlated) enhanced and/or non-enhanced image, such that theaverage intensity in the respective (corresponding) non-enhancedsub-regions is made equal. In this way, the (spatially correlated)normalised enhanced image and the (spatially correlated) normalisednon-enhanced image (also referred to as the “normalised spatiallycorrelated enhanced image” and the “normalised spatially correlatednon-enhanced image”) can be obtained. An embodiment of a normalisingoperation is described in more detail in the detailed description ofembodiments below. The term “average” as used here means in particularthe mean, mode or median. Once the spatially correlated enhanced imageand the spatially correlated non-enhanced image have been normalisedwith respect to each other, in particular by applying a normalisingoperation to the intensities of the voxels of at least one of these twoimages (i.e. the enhanced image and/or the non-enhanced image), adifference operation is preferably performed on the normalised spatiallycorrelated enhanced image and the normalised spatially correlatednon-enhanced image in order to determine an image which is then referredto as the “normalised difference image”, in particular by subtractingthe intensities of corresponding voxels from each other and assigningthe results of the subtractions as intensities to corresponding voxelsof the normalised difference image.

The structure of interest occupies a sub-region in the enhanced imagewhich in particular exhibits a signal intensity in the enhanced imagewhich significantly differs from the signal intensity exhibited in thecorresponding sub-region (within the common reference system) in thenon-enhanced image. The normalised difference image is intended toemphasise the differences in signal intensity. Thus, the normaliseddifference image allows the sub-regions which are candidates for thestructure of interest to be identified. In order to identify thestructure of interest, another step is preferably performed whichinvolves acquiring a condition for the intensity (referred to as the“intensity condition”). If this condition is fulfilled, then there is agreater likelihood that the voxels in the sub-regions in the normaliseddifference image (or in an image obtained by processing the normaliseddifference image, for example by using the aforementioned spatialfilter) are voxels of the structure of interest. One step of the dataprocessing method therefore preferably involves acquiring an intensitycondition for differentiating between structures of interest and otherstructures which do not exhibit an enhanced signal (in the enhancedimage and/or the normalised difference image or processed normaliseddifference image). The intensity condition can in particular be suchthat it is fulfilled if the intensity of voxels is above a predeterminedthreshold. The step of acquiring and applying the intensity conditiondoes not exclude the possibility of one or more other conditions alsobeing acquired and applied, i.e. at least one intensity condition (seebelow) is acquired and applied.

A step of “determining a binary image on the basis of the intensitycondition and the normalised difference image” in particular involvesapplying the intensity condition to the normalised difference image orto a processed normalised difference image, wherein processing thenormalised difference image in particular involves applying the spatialfilter. In accordance with one embodiment, therefore, the normaliseddifference image is subjected to a spatial filter (i.e. “processing”)which suppresses structures which are unlikely to relate to thestructure of interest. The spatial filter can in particular be designedsuch that thin and lengthy structures such as arteries or isolatedhigh-intensity voxels (which are due to statistical noise) can besuppressed, i.e. in accordance with one embodiment (explained in moredetail further below), the data processing method comprises theadditional steps of acquiring a spatial filter and then applying thespatial filter to the normalised difference image, before performing thesubsequent additional step described in the following paragraph.

In accordance with this additional step, the intensity condition is usedto determine a binary image from the normalised difference image or theprocessed normalised difference image, i.e. such that voxels in thedetermined binary image represent digital information only (forinstance, “YES” or “NO”). This means that the only information for eachvoxel contained in the binary image is that of whether the voxelrepresents an enhanced signal intensity or a non-enhanced signalintensity, i.e. whether the voxel fulfils the intensity condition ordoes not fulfil the intensity condition.

In accordance with another step, the binary image is used in order toacquire a sub-region within the binary image which is referred to as the“start region”. The start region can be acquired by receiving data whichdescribe the position and/or geometry (shape and/or size) of the startregion (for example via a user input) or by automatically determiningthe start region in the way described below. The start region is knownor assumed to comprise at least some of the structure of interest. Thisknowledge can for example be garnered from a longitudinal study of thetumour development within the patient, for example in the form of asequence of images over a longer period of time which correspondinglyshow the growth of a tumour. The start region can be selected by a useror automatically identified by a program, in particular in such a waythat the start region encompasses the tumour or at least a major part ofit. The start region can for example be automatically determined usingsymmetry properties of the anatomical body parts and/or significantdeviations between the intensity in the sub-region which is a candidatefor the start region and the intensity in other sub-regions, asdiscussed below.

In accordance with a particular feature of the present invention, thestart region does not represent a strict borderline for the structure ofinterest but rather defines a region from which to start a flexible andelaborate process for determining the structure of interest. This meansthat the process of determining the structure of interest in accordancewith the present invention is not significantly dependent on theposition and geometry of the start region (which may be selecteddifferently by different users). This makes the process of the presentinvention particularly robust.

As indicated above, acquiring the start region does not represent theend point of the data processing method; instead, the image elementswithin the start region are then used for further processing. The imageelements of the binary image which fulfil the intensity condition can beunderstood to represent “seed points” for this further processing. Theseimage elements are accordingly referred to as “seed image elements”. Allthe voxels within the start region of the binary image which fulfil theintensity condition(s) collectively represent a structure which isreferred to as the “start structure”.

In accordance with another step of the data processing method, the seedimage elements are used to expand the start structure if (and inparticular only if) certain conditions apply. Preferably, a so-called“region-growing algorithm” is applied in order to expand the startstructure. The expanding start structure is referred to as the “growingstructure”. The aforementioned seed elements serve as seed points forthe region-growing algorithm. The region-growing algorithm is designedto add one or more image elements of the binary image which fulfil theintensity condition to the growing structure if certain conditionsapply. Image elements represented in the binary image which fulfil theintensity condition are referred to as “enhanced binary image elements”.Enhanced binary image elements are added to the growing structure (only)if certain conditions are fulfilled. They are preferably added in steps,such that the growing structure grows progressively from the startstructure into an enlarged growing structure. In particular, enhancedbinary image elements added to the growing structure serve as additionalseed points for the region-growing algorithm. Since the start structurerepresents the agglomeration of enhanced binary image elements withinthe start region, the added image elements are preferably outside thestart region. The enhanced binary image elements are added to thegrowing structure (only) if certain conditions apply. One of theseconditions is that an enhanced binary image element is only added to thegrowing structure if the enhanced binary image element is adjacent tothe growing structure, wherein “adjacent” preferably means that theenhanced binary image element is a neighbouring image element of anenhanced binary image element which is already part of the growingstructure, wherein “neighbouring” in turn means in particular that thevoxel which represents the enhanced binary image element is preferablyin contact via a corner (in a two-dimensional image) or edge (in athree-dimensional image) and/or via an edge (in a two-dimensional image)or a face (in a three-dimensional image) with an enhanced binary imageelement which is already part of the growing structure. Anothercondition, which has already been mentioned, is that the image elementhas to be an enhanced binary image element. As also already mentionedabove, the image elements are added to the growing structure even if theenhanced binary image elements are outside the start region.

The growing procedure is performed in steps, as mentioned above, untilthe availability of enhanced binary image elements which fulfil at leastthe aforementioned conditions is exhausted. Other conditions, which arementioned below, can also be applied, in particular in order to preventthe growing structure from “protruding” (or “leaking” and in particularundesirably spreading), while the region-growing algorithm is beingapplied, into regions in which the structure of interest is assumed orknown to not be present, such as for example regions known or assumed tobelong to a “non-habitat structure” as described below (and for exampleidentified using a brain mask). This is explained in more detail furtherbelow. Alternatively or additionally, image elements which are unlikelyto belong to the structure of interest can be removed from the finalgrowing structure using particular procedures, as described in moredetail further below (see the section on “brain mask”), in order toprocess the final growing structure, i.e. determining the structure ofinterest on the basis of the final growing structure encompassesequating the structure of interest with the final growing structure orfurther processing the final growing structure (for example by applyingthe brain mask) and then equating the processed final growing structurewith the structure of interest. The structure obtained following thisremoving operation is then determined to be the structure of interest,i.e. the final growing structure is used as a basis for determining thestructure of interest in that it is either identified as being thestructure of interest or is used as the starting point for a particularprocedure, the result of which is identified as the structure ofinterest.

As mentioned above, the growing structure can be prevented fromprotruding as the region-growing algorithm is applied by enforcing atleast one other condition. In accordance with one embodiment, this othercondition is used to prevent the growing structure from spreading beyonda habitat structure (such as for example the white matter in a brain),i.e. from protruding into a non-habitat structure. The habitat structureis a structure in which the structure of interest is known or assumed tobe contained, and a non-habitat structure is a structure in which thestructure of interest is known or assumed to not be contained.

Any undesirable spread of the growing structure can also be reversed byremoving any image elements which do not belong to a habitat structurefrom the final growing structure. Alternatively or additionally, acondition can be enforced whereby image elements which belong to anon-habitat structure are not added. Alternatively or additionally,image elements which belong to a non-habitat structure can be removedfrom the final growing structure. If image elements are removed from thefinal growing structure, then the final growing structure which has beensubjected to the removing operation is preferably deemed to representthe structure of interest.

In accordance with another embodiment, the method comprises a step ofacquiring data which are referred to as “property data” and describespatial and/or representational properties of the habitat structureand/or the non-habitat structure. Spatial properties can compriseinformation on the relative position of the habitat structure and/ornon-habitat structure relative to anatomical structures represented in amedical image. In particular, the relative position is described by thespatial property data. The relative position of the skull is for exampledescribed relative to the white matter. The spatial propertiespreferably comprise information on the geometry of the habitat structureand/or non-habitat structure, such as for example the typical shape of askull. The property data can be described by an anatomical atlas whichdescribes the spatial properties of the habitat structure and/or thenon-habitat structure. The representational properties in particularcomprise information on the absolute intensity of the habitat structureand/or non-habitat structure in a medical image and/or information onthe relative intensity of the habitat structure and/or non-habitatstructure relative to anatomical structures represented in the medicalimage. The relative intensity is in particular described in terms of themedical imaging method used to generate the medical image (for example,the type of MRI, the magnetic field strength, etc.). If the medicalimaging method uses x-ray (such as for example CT), the representationalproperties also in particular comprise information on absoluteintensities (for example in Hounsfield units). The property data inparticular describe the properties of the habitat structure and/ornon-habitat structure in the enhanced image and/or non-enhanced imageand/or normalised difference image. The property data are preferablyused to identify the habitat structure and/or non-habitat structure,i.e. the spread of the growing structure can be prevented or reversed onthe basis of the property data, by determining whether an image elementbelongs to the habitat structure or the non-habitat structure. Theaforementioned enhanced image and/or non-enhanced image is/are inparticular a spatially correlated enhanced image and/or non-enhancedimage, respectively. Due to this spatial correlation, an image elementidentified as belonging to the habitat structure or the non-habitatstructure has a defined position in the common reference system. Thismeans that the corresponding image element in the binary image can alsobe identified as belonging to the habitat structure or the non-habitatstructure, respectively. Generally, there is preferably a spatialcorrelation between the image elements of the enhanced image, thenon-enhanced image, the normalised difference image and the binaryimage. This enables information on whether an image element in one ofthe images belongs to a habitat structure and/or a non-habitat structureto be transferred to a corresponding image element in another of theimages. Preferably, there is a bijective positional relationship betweenthe image elements (voxels) of the aforementioned images.

The geometrical properties of the non-habitat structure can for examplecorrespond to the geometrical properties of a vascular system and/or cancorrespond to single image elements which exhibit an intensity whichrepresents a spike in the intensity distribution of the surroundingimage elements, i.e. which deviates from the average intensity by morethan a defined threshold which is a function of the standard deviationof the variation in intensity. If, for example, the intensity of theimage element deviates by more than two standard deviations from theaverage intensity in its surroundings (which can be defined to comprisea predefined number of image elements), then it is assumed to representa statistical spike in the intensity distribution. To this end, thenormalised difference image is preferably processed, before the binaryimage is determined, in order to change the intensity of image elementswhich belong to structures exhibiting the aforementioned geometricalproperties and/or the aforementioned representational properties (suchas for example intensity spikes). To this end, a spatial filter ispreferably applied which is in particular a filter which performsgreyscale reconstruction (see in this respect the above-mentioned paperby L. Vincent). The intensity of the image elements is preferablychanged such that there is a greater likelihood that the changedintensity does not fulfil the intensity condition, in particular if theimage element with the changed intensity belongs to a non-habitatstructure and/or does not belong to a habitat structure. The changedintensity is in particular closer to the intensity threshold or passesthe intensity threshold (as compared to the situation before theintensity was changed) if the intensity condition was fulfilled beforethe intensity was changed. If the image element has been identified asbelonging to a structure which has geometrical properties similar to avascular system and/or if the image element represents an intensityspike, then it is in particular assumed that it does not belong to ahabitat structure and/or that it does belong to a non-habitat structure.By changing the intensity value of such image elements in such a waythat they do not fulfil the intensity condition, it is possible toprevent the growing structure from spreading into the parts occupied bythese image elements when the region-growing algorithm is applied.

In accordance with another embodiment, which is in particular based onthe aforementioned embodiments, the undesirable spread of the growingstructure is prevented by applying a mask to a medical image. The maskis preferably constituted to extract parts of the image which belong tothe habitat structure and/or to block parts of the medical image whichbelong to the non-habitat structure in order to only apply theregion-growing algorithm to image elements which belong to the habitatstructure and/or to block the participation of image elements whichbelong to the non-habitat structure in the region-growing algorithm. Theaforementioned medical image is in particular the non-enhanced imageand/or the enhanced image and/or the normalised difference image (and/orthe binary image). The enhanced image and/or the non-enhanced image isin particular the spatially correlated enhanced image and/or thespatially correlated non-enhanced image, respectively. The mask ispreferably based on spatial and/or representational properties asdescribed by the property data. An example of such a mask is the brainmask described in more detail in the detailed description of theembodiments further below. The mask can be generated automatically or byuser interaction.

In accordance with another embodiment, an anatomical atlas is used tosegment the habitat structure and/or non-habitat structure in a medicalimage. The medical image is preferably the (spatially correlated)non-enhanced image and/or the (spatially correlated) enhanced imageand/or the normalised difference image. The anatomical atlas is inparticular an example of property data and describes spatial and/orrepresentational properties of the habitat structure and/or thenon-habitat structure.

As already indicated above, the aforementioned undesirable spread of thegrowing structure is prevented by blocking image elements represented inthe binary image from participating in the region-growing algorithm,such that only image elements determined to belong to the non-habitatstructure participate. These image elements are preferably blockedbefore the region-growing algorithm is applied, for example by settingthe intensity of these image elements to a value which does not fulfilthe intensity condition. Alternatively or additionally, the imageelements can be blocked from participating in the region-growingalgorithm while it is being applied, by prohibiting these image elementsfrom being added to the growing structure.

Alternatively or additionally, the participation of image elements inthe region-growing algorithm can be restricted to particular imageelements. These particular image elements are preferably those whichhave been determined to belong to the habitat structure. This means thatregion-growing can only be performed within the habitat structure. Thisis preferably implemented before or while the region-growing algorithmis applied, for example by only allowing image elements to fulfil theintensity condition(s) if they belong to the habitat structure, or byextracting the image elements which belong to the habitat structurebefore applying the region-growing algorithm and then applying theregion-growing algorithm only to these extracted image elements.

Alternatively or additionally, image elements which have already becomepart of the growing structure, in particular part of the final growingstructure, can be removed, in particular after the final growingstructure has been determined, if they fulfil certain conditions, suchas for example in particular if they belong to the non-habitatstructure. This removing operation is preferably performed before thegrowing structure is determined to be the structure of interest.

In accordance with another embodiment, the non-enhanced sub-region isdetermined on the basis of a representation of a part (sub-region) ofthe habitat structure, i.e. the habitat structure is for exampleidentified using an anatomical atlas and the intensity distributionwithin the sub-region is then analysed. If the peaks of the intensitydistribution do not indicate an enhanced signal, and in particular ifthey do not exceed a predefined intensity level, then it is assumed thatthe sub-region does not include any potential candidate for a structureof interest and it is determined to be a non-enhanced sub-region.

In accordance with another embodiment, the start region is determined onthe basis of a symmetry analysis of the anatomical body part. If theanatomical body part exhibits symmetry properties, such as for example abrain which comprises a left and right hemisphere, then deviations fromthe symmetry properties within sub-regions can be used as an indicationthat a structure of interest is present in said sub-region. If, inparticular, the anatomical body part (including the structure ofinterest) comprises a mid-sagittal plane which divides the anatomicalbody part into a first and a second part, the symmetry properties of theanatomical body part can be used to determine the start region. If, inparticular, the structure of interest is located in one half (a firstpart) of the anatomical body part, then it is unlikely that anotherstructure of interest will also be found, symmetrically located, in theother (second) part. It is therefore likely that there will be adeviation in the symmetry properties between the left and right half ofthe anatomical body part. In both parts, there is a habitat structurewithin which the structure of interest may be located. In order todetermine the start region, the symmetry properties of the anatomicalpart are therefore analysed by comparing spatial and/or representationalproperties of the left and right half of the anatomical body part withrespect to a deviation in symmetry. Before the analysis with respect tosymmetry is performed, the three-dimensional medical image is preferablyprocessed in order to cut it into two-dimensional layers. Thetwo-dimensional layers are preferably cut such that the symmetry plane(for example, the mid-sagittal plane) passes perpendicularly through thelayers. The layers are then respectively analysed with respect to theirsymmetry, i.e. the left half is compared with the right half. The higherthe deviation in the spatial (in particular geometrical) and/orrepresentational (in particular intensity) properties, the greater thecertainty that the corresponding layer is not symmetrical.

As mentioned above, the intensity condition is preferably acquired. Theintensity condition can for example be acquired manually via a userinput or also, in accordance with one embodiment, automatically on thebasis of the enhanced image. To this end, an enhanced sub-region whichrepresents a calibration structure in the enhanced image is preferablydetermined. The calibration structure is in particular not the structureof interest. Nevertheless, the intensity of the calibration structure asrepresented in the (spatially correlated) enhanced image differssignificantly from the intensity of the calibration structure in the(spatially correlated) non-enhanced image. This difference in intensityis preferably known or assumed to be reproducible, such that thedifference in intensity can be used as a basis for determining theintensity condition, i.e. the intensity, in particular the intensitydistribution within the calibration structure as represented in theenhanced image, is used as a basis for determining the threshold.

In accordance with one embodiment, the tumour can comprise metastaseswhich are spatially distinct from the main body of the tumour. In thiscase, the method of the present invention preferably does notnecessarily include the following steps (i.e. the following steps,mentioned in Claim 1, are not essential):

-   -   acquiring a sub-region within the binary image which is referred        to as the start region and which is known or assumed to comprise        at least a part of the structure of interest;    -   determining image elements of the binary image within the start        region which fulfil the intensity condition, wherein these image        elements are referred to as seed image elements and collectively        represent a structure which is referred to as the start        structure;    -   determining a structure referred to as the final growing        structure, on the basis of the binary image using a        region-growing algorithm which starts with the seed image        elements which serve as seed points for the region-growing        algorithm, wherein the region-growing algorithm is constituted        to add image elements represented in the binary image to the        start structure, which is then referred to as the growing        structure and is equal to the start structure before the image        elements are added, wherein the added image elements serve as        additional seed points for the region-growing algorithm, and the        region-growing algorithm is constituted to add image elements of        the binary image to the growing structure if at least the        following conditions are fulfilled:        -   the image elements represented in the binary image are            adjacent to the growing structure; and        -   the intensity of the image elements of the binary image            represent an enhanced signal intensity, the image elements            being added until no further image elements of the binary            image are to be added to the growing structure in accordance            with the region-growing algorithm, wherein the growing            structure is then the final growing structure; and    -   determining the structure of interest on the basis of the final        growing structure.

This means that in this case, after the binary image has beendetermined, steps other than the aforementioned steps can be performedin order to determine the exhibited metastases. The following steps canof course be combined with the aforementioned steps in order todetermine both the tumour and the spatially distinct metastases. Themetastases generally have particular geometrical properties which arepreferably used in order to determine the metastases.

Data referred to as metastasis geometry data are preferably acquired.These metastasis geometry data describe geometrical properties of themetastases and in particular the geometrical properties of themetastases in image layers. The binary image is preferably athree-dimensional image which comprises a plurality of two-dimensionalimage layers. The image layers respectively comprise a plurality ofpixels. The metastasis geometry data describe the geometrical propertiesof the metastases in the image layers. These geometrical propertiesinclude in particular relative geometrical properties which describe therelative variation in the geometry of a metastasis of one image layerwith respect to another image layer and in particular from one imagelayer to an immediately adjacent, i.e. neighbouring, image layer.

Preferably, a set of adjacent (in particular neighbouring) enhancedimage elements is within at least one of the image layers, preferably inseveral neighbouring image layers. The set of adjacent image elements ispreferably integrally closed and preferably set apart from other setsand/or other image elements which represent the enhanced signalintensity. The sets are then analysed with respect to their geometricalproperties in the image layers, in particular with respect to theirtwo-dimensional properties in the respective image layers. A set ispreferably determined to represent a metastasis if it exhibitstwo-dimensional geometrical properties which match the geometricalproperties described by the metastasis geometrical data. The requiredgeometrical property may for example be that the borderline of the setexhibits a shape which is similar to a circle.

In accordance with another embodiment, which is in particular based onthe aforementioned embodiment, the sets are analysed with respect totheir relative geometrical properties when comparing one image layerwith another image layer. A set within one image layer is referred to asa metastasis slice. Preferably, the metastasis geometry data describehow a set is identified as a metastasis if it exhibits at least one ofthe features described in the following. These features are inparticular features which reflect the fact that the combined metastasisslices are similar to a spherical shape.

In accordance with one geometry feature, the metastasis slices arepresent in adjacent (neighbouring) image layers. The metastasis slicesare in particular described as being arranged one above the other inadjacent (neighbouring) image layers. The metastasis slices are inparticular arranged concentrically. Preferably, the metastasis geometrydata describe how a larger metastasis slice encompasses smallermetastasis slices in the other, adjacent (neighbouring) image layers ifviewed in a direction perpendicular to the plane of the image layer.Preferably, the metastasis geometry data describe how the size of ametastasis slice between two other metastasis slices (where all threeare arranged one above the other) preferably differs in that the size ofthe middle metastasis slice is larger than at least one of the other twometastasis slices. This condition represents the spherical shape usuallyobserved in metastases. In particular, the middle metastasis slicerepresents a maximum size or a medium size between the sizes of theother two neighbouring (outer) metastasis slices. If viewed in adirection perpendicular to the image layers, the larger of theneighbouring metastasis slices preferably encompasses the smallerneighbouring metastasis slice. The metastasis geometry data preferablydescribe how at least one and in particular only one of the metastasisslices arranged one above the other is larger than the two neighbouringmetastasis slices.

The invention also relates to a program which, when running on acomputer, causes the computer to perform one or more or all of themethod steps described herein and/or to a program storage medium onwhich the program is stored (in particular in a non-transitory form)and/or to a computer comprising said program storage medium and/or to a(physical, in particular electrical, in particular technicallygenerated) signal wave, in particular a digital signal wave, carryinginformation which represents the program, in particular theaforementioned program, which in particular comprises code means whichare adapted to perform any or all of the method steps described herein.

The present invention is also directed to a medical image processingsystem for determining a structure of interest. The medical imageprocessing system comprises at least one analytical device forgenerating medical images of an anatomical body part by means of amedical imaging method. The medical image processing system alsocomprises a computer which is constituted to process the generatedimages by running the aforementioned program. The computer comprises aprogram storage medium on which the aforementioned program is loaded.Running the program allows a structure of interest to be determined onthe basis of the generated images.

Additional features of the present invention are disclosed in thefollowing detailed description.

FIG. 1 schematically shows the steps of an embodiment of the method ofthe present invention.

FIGS. 2A-C illustrate some of the steps of the method of the presentinvention.

FIGS. 3A-D illustrate some of the steps of the method of the presentinvention.

FIG. 4 illustrates the so-called protrusion which may occur duringregion growing.

FIG. 5 illustrates how protrusion is suppressed.

FIGS. 6A-B illustrate how protrusion is suppressed by greyscalereconstruction.

FIGS. 7A-E illustrate how a metastasis is detected.

FIG. 8 schematically shows a medical image processing system.

FIG. 1 schematically shows the steps of an embodiment of the dataprocessing method of the present invention. In a first step S10, twoimages are acquired. One of the two images (shown in FIG. 2A) isgenerated by administering a contrast agent to the patient in order toimprove the visibility of particular internal body structures which areof interest and therefore referred to as “structures of interest”. Inthe following, the structure of interest is assumed for the sake ofexample to be a tumour. The structure of interest is situated within ananatomical body part of the patient. The medical imaging method is inparticular a magnetic resonance imaging method or x-ray computertomography (CT) imaging method. In the example shown in FIG. 1, theresulting image (referred to as an enhanced image) is a T1-weightedimage (also referred to as a T1-weighted scan). The T1-weighted scangenerated using a contrast agent is denoted as “T1w+c” in FIGS. 2A-C.

Another image, which is a non-enhanced image, is also acquired in stepS10. The non-enhanced image is generated without using a contrast agent.The non-enhanced image is in particular generated using a magneticresonance imaging (MRI) method. The image is preferably of the samemodality as the enhanced image, i.e. in the example of FIG. 1, thenon-enhanced image is preferably also a T1-weighted image (also referredto as a T1-weighted scan).

In a following step S20, the non-enhanced image (also referred to asT1w−c, as for example in FIGS. 2A-C) and the enhanced image (T1w+c) arepreferably spatially correlated, in particular registered, with respectto each other, preferably by means of image fusion. One particularresult of spatial correlation is that the non-enhanced image and theenhanced image are described in a common reference system. The imagescan be three-dimensional or two-dimensional images, but are preferablythree-dimensional images or sets (in particular stacks) oftwo-dimensional images (such as for example two-dimensional layers ofvoxels positioned one above the other as described with respect to FIGS.7A-E) which describe two-dimensional features of the samethree-dimensional structure (for example, the head) and which are inparticular positioned one above the other. Preferably, rigid or elasticimage fusion is selected, depending on the type of anatomical body partin question, as represented by the images. If, for example, the imagesrepresent the head of a patient (or part of it), then rigid image fusionis preferred in accordance with one embodiment. Elastic fusion ispreferred for images representing extra-cranial body parts.

While performing image fusion or in a separate, either preceding orsubsequent step, at least one of the enhanced image and the non-enhancedimage is preferably transformed in such a way that there is a bijectiveassignment between the respective image elements (in particular, thevoxels) of the enhanced image and the non-enhanced image once thetransformation is complete. In the following, the image elements arereferred to as voxels by way of example only. In particular, layersresulting from the images generated (by MRI) have a one-to-onecorrespondence once the transformation is complete. In order to achievethis bijective relationship (in particular, the one-to-onecorrespondence between the layers, in particular the voxels), aninterpolation (for example, a cubic or spline interpolation) ispreferably used to calculate the position of the voxels and/or thecolour value (in particular, the intensity) assigned to the respectivevoxels, so that a one-to-one correspondence between the voxels can beestablished.

The acquired images have preferably already been processed in accordancewith standard procedures to eliminate brightness variations due to theimaging method, such as for example intensity inhomogeneity in the caseof MRI images, and in particular to adjust the brightness of anatomicstructures belonging to the same class of tissue. These procedures inparticular involve correcting inhomogeneity, as for example in an “RFinhomogeneity correction” or a “bias field correction”. One suchstandard procedure is known as “N3”.

In a following step S30, the normalisation difference image ispreferably calculated. In the example shown in FIG. 2A, the MRI image isan image of the patient's head. The brain in particular is shown in theMRI image. A so-called “non-tumour region” (NTR in FIG. 2A) ispreferably acquired (in particular selected) in both the enhanced imageand the non-enhanced image. This region is encircled in FIG. 2A and usedfor the normalisation process. It is necessary to normalise MRI imagesin particular, since their intensity values are not normalised (unlikeCT images). The non-tumour region is a region which does not exhibit anenhanced signal; in particular, the contrast agent has no effect on theintensity of voxels in the non-tumour region. The non-tumour region isan example of the “non-enhanced sub-region”.

The non-tumour region can be selected by a user. In accordance withanother embodiment, however, the non-tumour region is selectedautomatically, in particular in the case of a symmetrical anatomicalbody part such as the brain. The brain exhibits symmetrical properties.Preferably, tissue within which the tumour could be embedded isidentified, for instance using an anatomical atlas. If the tumour isonly present on one side of the brain (i.e. on one side of themid-sagittal plane), then white matter (which is non-enhanced) situatedin particular very distant from the suspected tumour region, inparticular on the other side of the mid-sagittal plane, can be used as anon-tumour region. The white-matter region is in particular identifiedusing an anatomical atlas. The non-tumour region can in particular beidentified by detecting intensity and/or symmetry deviations between thetwo sides of the brain. The symmetry analysis is in particular performedon layers of the brain. The aforementioned steps can be performedautomatically in order to automatically identify a region of interestand/or a non-tumour region.

In a sub-step of step S30, the intensity distribution within thenon-tumour region in the enhanced image and the intensity distributionwithin the non-tumour region in the non-enhanced image is analysed. Thecorresponding distribution is shown in FIG. 2B and denoted as“histograms of non-tumour region”. An average intensity deviationbetween the two non-tumour regions is determined on the basis of thesehistograms, and the images are normalised on the basis of this average.Any kind of average, such as a mean, mode or median, can be used. In theexample shown in FIGS. 2A-C, the average intensity—preferably, the mode(m1 and m2, as shown in FIG. 2B)—is determined for each of the intensitydistributions (in particular, histograms).

In a subsequent sub-step of Figure S30, a normalised difference image(NDI in FIG. 2C) is determined by applying the following equation toeach of the corresponding voxels of the enhanced image and thenon-enhanced image, in order to calculate the intensity—referred to as“ID”—of the corresponding voxel of the normalised difference image, inparticular based on the established one-to-one correspondence betweenthe voxels of the enhanced and non-enhanced image:

ID=(I2−A2)−(I1−A1)

where I1 is the intensity of the voxel of the enhanced image or thenon-enhanced image, but preferably the non-enhanced image, A1 is theaverage intensity value of this image (for example, the mode m1), I2 isthe intensity of the corresponding voxel of the other image (thenon-enhanced image or the enhanced image, but preferably the enhancedimage) and A2 is the average intensity value of said other image (forexample, the mode m2). The calculation is preferably performed for all(or most) of the voxels of the enhanced image and the respectivelycorresponding voxels of the non-enhanced image. In the example of FIG.2C, m1=A1 and m2=A2. The normalised difference image exhibits anincrease in contrast between non-enhanced image parts and enhanced imageparts as compared to the enhanced image.

The equation is preferably applied in such a way that the resultingdifference in the intensity of voxels representing the structure ofinterest (for example, a tumour) is positive. In the case of T1 images,the use of contrast agents generally results in an increase inintensity. In this case, therefore, I1 is preferably the voxel intensityof the non-enhanced image and I2 the voxel intensity of the enhancedimage. However, it is also possible for the use of contrast agent tolower the signal intensity. This is in particular the case with T2images which for example use barium sulphate as the contrast agent. Inthis case, I1 in the above-mentioned equation is preferably the voxelintensity of the enhanced image and I2 is preferably the voxel intensityof the non-enhanced image. If the intensity ID of a voxel of thenormalised difference image becomes negative, then it is preferably setto zero.

Applying the above-mentioned equation to the voxels of the enhancedimage and the non-enhanced image results in the normalised differenceimage shown in FIG. 2C. As can be seen from FIG. 2C, the visibility ofthe tumour 100 has been increased as compared to the visibility of thetumour 100 in the enhanced image (T1w+c) shown in FIG. 2A. Inparticular, the contrast between the non-enhanced region and theenhanced region is increased in the normalised difference image (“NDI”)as compared to the enhanced image.

In order to further clarify which voxels exhibit an intensity caused bythe contrast agent and which do not, an intensity condition—inparticular, a threshold—is acquired in step S40 in order to be able todifferentiate between voxel intensities caused by the contrast agent andvoxel intensities not caused by the contrast agent. In accordance withone embodiment, such an intensity condition can be acquired by means ofa user input. In accordance with another embodiment, the intensitycondition can be determined by referring to a body region which exhibitsa known and reliable intensity response to the contrast agent in anenhanced image. In the case of the head, in particular the brain andmore particularly a brain tumour, one such corresponding region is inparticular the nasal mucosa. In the case of non-brain regions, one suchcorresponding region is in particular the thoracic aorta.

FIG. 3A shows an enhancement of the surrounding tissue of the nasalcavity (denoted by the reference sign 110) due to the use of contrastagent. FIG. 3D shows the intensity distribution (in particular, ahistogram) of the voxels representing the nasal cavity (i.e. the nasalmucosa). When determining the threshold, the voxels having the highestintensity values are preferably excluded. In accordance with a firstembodiment, a predetermined percentage (x % quantile) of voxels isexcluded, such that only voxels of lower intensities remain. Thepercentage of excluded (higher-intensity) voxels is preferably higherthan 1%, in particular higher than 3%, and/or lower than 30%, inparticular lower than 15% and more particularly lower than 7%.

In accordance with said first embodiment, the voxel having the highestintensity is determined from the remaining (non-excluded) voxels, and athreshold is determined on the basis of this intensity, as apredetermined percentage of the intensity of said voxel. This percentageis preferably higher than 10%, in particular higher than 20%, and/orpreferably lower than 40%, in particular lower than 30%.

In accordance with a second embodiment, a percentage of voxelsexhibiting the lowest intensities within the intensity distribution(FIG. 3D) is determined, and the threshold is set at the voxelexhibiting the highest intensity within these low-intensity voxels. Thispredetermined percentage is preferably higher than 10%, in particularhigher than 20%, and/or preferably lower than 40%, in particular lowerthan 30%.

In accordance with another embodiment, the intensity condition is notdescribed by an exact value in the form of a threshold but is insteaddefined by combining a threshold value with a noise function, whereinthe value of the noise function is dependent on the position of thevoxel.

FIG. 3B illustrates the normalised difference image NDI which isidentical to that shown in FIG. 2C. The threshold determined inaccordance with FIG. 3D) is applied to the normalised difference imageNDI of FIG. 3B, i.e. to all the voxels of the image, in order todetermine the binary image BI shown in FIG. 3C, i.e. each voxel in thebinary image is determined as either representing an enhanced signalintensity or as not representing an enhanced signal intensity, hence theterm “binary” image. In FIG. 3C), the voxels determined as representingan enhanced signal intensity (also referred to as “enhanced voxels”) areshown in black. The tumour 100 in the binary image BI is therefore alsoshown in black. The voxels determined as representing a non-enhancedsignal intensity (also referred to as “non-enhanced voxels”) are shownin white.

Additional explanations with respect to steps S10 to S40 can be found inKanaly, C. et al.: “A Novel Method for Volumetric MRI ResponseAssessment of Enhancing Brain Tumor” in PLOS ONE (an online publication)6(1), 2011.

FIG. 4 shows another example of a binary image BI′. The enhanced voxelsare enclosed by continuous lines. There are two types of enhanced voxelsin FIG. 4. The dotted area 210 represents enhanced voxels, and thehatched area 220 also represents enhanced voxels. The non-enhancedvoxels are shown in white. A dashed circular line surrounds a so-calledtumour start region (“TSR” in FIG. 4) which is also referred to hereinsimply as the “start region”. The tumour start region TSR is acquired instep S50 of the method shown in FIG. 1. The data which describe theposition and geometry of the tumour start region in the binary image canbe acquired by a user input or also automatically. If the anatomicalbody part (as represented in the binary image) has symmetricalproperties, the image can be analysed for deviations from this symmetry.The parts of the image which deviate from the symmetrical properties canbe identified as a tumour start region. This procedure is preferablyapplied if the tumour is only present in one half of the symmetricalstructure, for instance on one side of the mid-sagittal plane. Thetumour start region is preferably determined as the region where thereis the highest concentration of enhanced voxels in the image. The tumourstart region TSR is preferably selected so as to include at least someof the tumour. The tumour start region TSR is preferably selected so asto include more enhanced voxels than non-enhanced voxels. In particular,the tumour start region can be determined on the basis of automaticallydetecting the tumour using automatic pathological detection and/or alongitudinal study. The tumour start region is in particular the mostrecently found region in which the tumour is present.

The tumour start region can for example be automatically determined asfollows. A geometrical structure such as a sphere or a cube is grown inaccordance with a predetermined procedure. For example, the radius ofthe sphere is successively increased by one voxel around a centre voxel.If the number of non-enhanced voxels grows more significantly than thenumber of enhanced voxels as the radius increases, then growing thegeometric structure (sphere) is discontinued and the geometric structure(sphere) at the time of discontinuance is deemed to be a candidatetumour start region. This process is repeated for all the voxels. Thecandidate structure (sphere) which comprises the most enhanced voxels isthen used as a start region. This is just one possible way ofautomatically determining the tumour start region.

Once the tumour start region has been acquired in accordance with stepS50, a region-growing algorithm is then applied in step S60, startingwith the voxels (image elements) in the tumour start region TSR. Thevoxels within the tumour start region TSR represent “seed points” forthe region-growing algorithm. Preferably, each voxel in the start regionrepresents a seed point for the region-growing algorithm. The enhancedvoxels (i.e. those with an assigned value indicating an enhanced signal)within the tumour start region collectively define a structure which isreferred to as the start structure, i.e. the set of enhanced voxelswithin the start region TSR represents the start structure. When theregion-growing algorithm is applied, any enhanced voxel adjacent to thestart structure is added to the start structure, hence the startstructure “grows”. All the enhanced voxels inside the start region (i.e.the start structure) have in particular been identified. This means inparticular that only enhanced voxels outside the start region are addedto the start structure in order to grow the start structure. A startstructure which has grown due to the addition of enhanced voxels isreferred to as a “growing structure”, i.e. the region-growing algorithmadds enhanced voxels to the growing structure if the enhanced voxels areadjacent to the growing structure and in particular outside the startregion. The region-growing algorithm is discontinued once there are nofurther enhanced voxels which are adjacent to the growing structure.Once the region-growing algorithm has been completed, the growingstructure is then referred to as the final growing structure. The term“adjacent” as used herein is in particular understood to mean that avoxel is adjacent to a region, in particular to the growing structure,if the voxel is a neighbour of at least one other voxel of the region(in particular the growing structure). In two dimensions, for example, aneighbourhood can be a neighbourhood of four voxels (i.e. on theabutting sides of the voxel) or a neighbourhood of eight voxels (i.e.four voxels which exhibit abutting sides and four voxels which areconnected via corners). Correspondingly, the neighbourhood of one voxelin three dimensions can consist of six voxels (the abutting surfaces) ortwenty-six voxels (the abutting surfaces and the contacting corners andedges).

In the example shown in FIG. 4, the region-growing algorithm results inthe growing structure “protruding” (in particular “leaking”) beyond thetumour start region when the region growing algorithm is applied to thestart structure. The final growing structure which results from theapplication of the region-growing algorithm is shown as a hatched regionin FIG. 4 and provided with the reference sign 220. The final growingstructure 220 comprises substructures 221, 222, 223 and 224 which arelinked by constrictions 225, 226 and 227. These substructures andconstrictions are candidates for forming part of the tumour and aretherefore referred to as candidate structures. Of the candidatestructures, the substructure 221 is assumed to represent the structureof interest, such as for example the tumour, since a part 221′ of thestructure is located inside the start region TSR. This part 221′therefore constitutes the start structure. In accordance with oneembodiment of the invention, the region-growing algorithm is modified(in particular supplemented by an additional step) in order to preventcandidate structures from protruding into non-habitat structures (seebelow) or beyond a habitat structure (see in particular the descriptionpertaining to FIG. 5 below) or in order to reverse such a protrusionafter it has been caused by the region growing algorithm.

The final growing structure can be modified by preventing the spread ofthe growing structure, in particular by restricting the application ofthe region-growing algorithm to parts of the binary image before orwhile the region-growing algorithm is applied and/or by changing theintensity of voxels in such a way that they do not fulfil the intensitycondition before or while the region-growing algorithm is applied.Alternatively or additionally, the final growing structure can bemodified after the region-growing application has been applied, byremoving substructures from the final growing structure which are deemedto not represent a tumour, i.e. by reversing the protrusion (“leakage”)manifested by these non-tumour substructures.

Embodiments in which the growing structure is modified before and/orafter the region-growing algorithm is applied are discussed below.

In FIG. 5, the binary image BI″ contains both a dotted structure 230 anda hatched structure 220′. The hatched structure 220′ is smaller than thehatched structure 220 shown in FIG. 4. The structures 220′ and 230 bothconsist of enhanced voxels. However, only the structure 220′ has beenidentified as a structure of interest, i.e. a tumour. In particular, thecandidate structures denoted in FIG. 4 as 222 and 224 and the candidatestructure denoted in FIG. 5 as 223″ (which in FIG. 4 is part of thesubstructure 223) have been determined as belonging to the structure230, i.e. as not representing the structure of interest (tumour), suchthat only the substructures 221 and 223′ (which is the other part of thesubstructure denoted as 223 in FIG. 4) and the constriction 226 aredetermined as being part of the hatched structure 220′, i.e. asrepresenting the structure of interest. The other substructures havebeen excluded by applying a brain mask to the binary image BP of FIG. 4.The brain mask is designed to exclude all the candidate structures orparts of candidate structures which are part of the head but not thebrain, such as for instance the eyes, skin, fat and bone. These partscan also be described as representing a “non-habitat structure”, whilethe brain represents a “habitat structure” (with the exception ofstructures such as the vasculature). The terms “habitat structure” and“non-habitat structure” will be explained in more detail below. Applyingthe brain mask (in one particular procedure) therefore preferablyremoves a non-habitat structure from the binary image and/or extracts astructure from the binary image which at least primarily consists of ahabitat structure. The brain mask is preferably applied before theregion-growing algorithm. In this way, the growing structure can beprevented from protruding into particular sub-regions of the body partsuch as for example the region 237 in FIG. 5 (i.e. the brain). Theseparticular sub-regions such as 237 are within the habitat structure andare linked to the start structure by the region-growing algorithm if thebrain mask is not applied first. Once the brain mask has been applied,this link is eliminated, and these particular sub-regions can then beidentified as not representing the structure of interest, since they arenot linked to the start structure. Accordingly, they can then be removedfrom the final growing structure. The brain mask is preferablydetermined on the basis of the non-enhanced image but can also bedetermined on the basis of the enhanced image and/or the normaliseddifference image. The brain mask can be determined by so-called “skullstripping”.

An anatomical atlas, in particular the so-called universal atlas(international patent application No. PCT/EP2012/071241 andinternational patent application No. PCT/EP2012/071239, both filed on 26Oct. 2012), can be used to determine the brain mask by segmenting theparts of the head which contain brain tissue and combining the segmentedparts to form the brain mask. Skull stripping procedures are describedin Fennema-Notestine C. et al.: “Quantitative Evaluation of AutomatedSkull-Stripping Methods Applied to Contemporary and Legacy Images:Effects of Diagnosis, Bias Correction, and Slice Location” in HumanBrain Mapping, Volume 27, Issue 2, February 2006, pages 99-113.

Alternatively, structures representing the brain and/or structures notrepresenting the brain are segmented in one of the aforementioned images(the enhanced image, the non-enhanced image or the normalised differenceimage, but preferably the non-enhanced image) using an anatomical atlas.The segmentation process relies in particular on the geometricalproperties and/or representational properties of the segmentedstructures. The atlas represents an example of property data whichdescribe geometrical and/or representational properties of structures ofthe anatomical body part. In accordance with one embodiment, theseproperties are in particular used to extract a first type of structuresin which the tumour is assumed or known to be embedded (and in which inparticular the tumour can grow, such as for instance the brain tissue inthe case of a brain tumour). The atlas can also describe the propertiesof a second type of structure in which the tumour is assumed to notreside and in particular in which it does not grow (such as fat, boneand dura mater in the case of a brain tumour). The set of structures ofthe first type is referred to here as the habitat structure, and the setof structures of the second type is referred to here as the non-habitatstructure. In other words, the tumour is assumed or known to be presentin a habitat structure but not in a non-habitat structure.

The geometrical properties and/or representational properties ofstructures can also be used to further process the normalised differenceimage before applying the intensity condition (threshold) to thenormalised difference image.

In a following step S70, so-called greyscale reconstruction is used toremove intensity variations in the normalised difference image whichcould or would fulfil the intensity condition but which are neverthelessassumed to not represent a structure of interest due to theirgeometrical properties and in particular because they are located in anon-habitat structure. The intensity variations represent deviationsfrom the background intensity. Applying greyscale reconstruction reducesand in particular eliminates this deviation. The structures which arerepresented by the intensity variations and are to be changed, inparticular reduced to the background intensity, by greyscalereconstruction are in particular those which have geometrical propertieswhich can be eliminated by applying a spatial filter to the normaliseddifference image. The spatial filter eliminates image features whichhave a high spatial frequency such as thin, lengthy structures (whichfor example represent vascular structures) which in particular have aramification of branches. Preferably, lengthy structures having anaverage cross-section of less than 2 mm or less than 1 mm in theirlongitudinal extension are suppressed by applying greyscalereconstruction, in particular by applying the spatial filter.

With respect to greyscale reconstruction, reference is made inparticular to Vincent, L.: “Morphological Grayscale Reconstruction inImage Analysis: Applications and Efficient Algorithms.”, in IEEETransactions on Image Processing, 2(2), 1993.

The left-hand side in FIG. 6A shows a tumour 100, a vascular structure110 and a metastasis 120 before greyscale reconstruction is applied. Theright-hand side in FIG. 6B shows the normalised difference image aftergreyscale reconstruction has been applied. As can be seen from acomparison of FIGS. 6A and 6B, the vascular structure 110 has beensuppressed and only smaller, lower-intensity structures 110′ remain,while the larger structures such as the tumour 100 and the metastasis120 (which are not thin, lengthy structures) remain unchanged. Ideally,the remaining vascular structures 110′ would have intensities which donot fulfil the intensity condition, such that the vascular structures110′ would be completely eliminated once the threshold has been applied,while the bulky structures of the tumour 100 and the metastasis 120fulfil the intensity condition and are thus still visible in the binaryimage. The enhanced tumour is thus determined in accordance with thesecriteria in step S80.

FIGS. 7A-E illustrate how a metastasis is detected. FIGS. 7A-C show themetastasis 120 as represented by different two-dimensional layers ofvoxel sets of the binary image. The voxel sets respectively comprisevoxels which indicate an enhanced signal intensity and are integrallyclosed, i.e. not adjacent to other voxel sets and/or enhanced voxels.The layers are referred to as metastasis slices and are located oneabove the other. The slice −2 shown in FIG. 7A is the topmost slice. Itis followed by a slice −1 which is shown in FIG. 7B. Below this slice,there is a slice 0 which is shown in FIG. 7C. This is followed by aslice +1 which is shown in FIG. 7D. Lastly, there is a slice +2 which isshown in FIG. 7E. As can be seen from FIGS. 7A-E as a whole, the size ofthe metastasis slices increases from slice −2 in FIG. 7A to slice 0 inFIG. 7C and then decreases from slice 0 in FIG. 7C to slice +2 in FIG.7E. The slices are preferably analysed in order to check whether themetastasis is similar to a predefined (three-dimensional) geometry, inparticular a sphere. In FIGS. 7A-E, the geometry of the metastasis 120and the thickness of the slices are such that the metastasis has a shapewhich is similar to a sphere. The shape represented by the differentslices is preferably analysed as to whether or not it is similar to apredetermined two-dimensional geometry (for example, a circle). Inparticular, the 2D geometry is analysed as to whether or not it is atwo-dimensional manifestation of the predefined three-dimensionalgeometry. The 3D geometry and in particular the 2D geometry of themetastasis is described by metastasis geometry data. The relative sizesand positions of the circles in the different slices shown in FIGS. 7Ato 7E are preferably also determined. A condition for such a positionalrelationship can for example be that the deviation in the centre of thecircle from layer to layer is within a predetermined range of variation.The centre is indicated in each of FIGS. 7A to 7E by the reference signC and the respective slice number, i.e. there is a centre at C−2, C−1,C0, C+1 and C+2. These are the centres of circles (not shown) which arepreferably fitted to the boundary of the respective metastasis in FIGS.7A to 7E. Preferably, the centres are located at least approximately oneabove the other. If, for example, all the centres are projected onto theplane of slice 0, then in accordance with a condition for thegeometrical properties of the metastasis, all the centres are within acircle around the centre C0 which has a radius which is smaller than apredetermined percentage of the radius of the circle fitted to themetastasis 120 in slice 0. This percentage is preferably set to be lowerthan 20% or 10%.

Additionally, a condition can be set for fitting the circle to theboundary of the metastasis in the different layers. The standarddeviation between the boundary and the circle can for example bedetermined so as to be less than a predetermined percentage of theradius of the fitted circle. This predetermined percentage is preferablylower than 20% or lower than 10%.

Aside from the above-mentioned conditions for the geometry of ametastasis, a condition for the size relationship can alternatively orpreferably additionally be set. This condition can for example stipulatethat the radius of the circle steadily decrease from a middle layertowards the outer layers, preferably in a manner which complies with thepredetermined geometrical properties of a metastasis, i.e. which forexample complies with a spherical shape of the metastasis.

FIG. 8 schematically shows a medical image processing system 300comprising an analytical device 310 for generating a medical image of apatient 320 lying on a patient couch 330. The medical image processingsystem 300 also comprises a computer 340 which is connected to theanalytical device 310, and a display device 342 which is connected tothe computer 340. The aforementioned method is performed by means of aprogram which is loaded into the computer 340 and which can be run onthe computer 340.

Definitions

The step of “determining an enhancing structure of interest” inparticular means determining a representation of the enhancing structureof interest and in particular involves determining spatial information(which in particular includes the spatial position and/or geometry)concerning a part within a medical image, wherein said part isdetermined (in accordance with the present invention) to represent theenhancing structure of interest.

The invention also relates to a program which, when running on acomputer, causes the computer to perform one or more or all of themethod steps described herein and/or to a program storage medium onwhich the program is stored (in particular in a non-transitory form)and/or to a computer comprising said program storage medium and/or to a(physical, in particular electrical, and in particular technicallygenerated) signal wave, in particular a digital signal wave, carryinginformation which represents the program, in particular theaforementioned program, which in particular comprises code means whichare adapted to perform any or all of the method steps described herein.

Within the framework of the invention, computer program elements can beembodied by hardware and/or software (this includes firmware, residentsoftware, micro-code, etc.). Within the framework of the invention,computer program elements can take the form of a computer programproduct which can be embodied by a computer-usable, in particularcomputer-readable data storage medium comprising computer-usable, inparticular computer-readable program instructions, “code” or a “computerprogram” embodied in said data storage medium for use on or inconnection with the instruction-executing system. Such a system can be acomputer; a computer can be a data processing device comprising meansfor executing the computer program elements and/or the program inaccordance with the invention, in particular a data processing devicecomprising a digital processor (central processing unit or CPU) whichexecutes the computer program elements, and optionally a volatile memory(in particular a random access memory or RAM) for storing data used forand/or produced by executing the computer program elements. Within theframework of the present invention, a computer-usable, in particularcomputer-readable data storage medium can be any data storage mediumwhich can include, store, communicate, propagate or transport theprogram for use on or in connection with the instruction-executingsystem, apparatus or device. The computer-usable, in particularcomputer-readable data storage medium can for example be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infraredor semiconductor system, apparatus or device or a medium of propagationsuch as for example the Internet. The computer-usable orcomputer-readable data storage medium could even for example be paper oranother suitable medium onto which the program is printed, since theprogram could be electronically captured, for example by opticallyscanning the paper or other suitable medium, and then compiled,interpreted or otherwise processed in a suitable manner. The datastorage medium is preferably a non-volatile data storage medium. Thecomputer program product and any software and/or hardware described hereform the various means for performing the functions of the invention inthe example embodiments. The computer and/or data processing device canin particular include a guidance information device which includes meansfor outputting guidance information. The guidance information can beoutputted, for example to a user, visually by a visual indicating means(for example, a monitor and/or a lamp) and/or acoustically by anacoustic indicating means (for example, a loudspeaker and/or a digitalspeech output device) and/or tactilely by a tactile indicating means(for example, a vibrating element or a vibration element incorporatedinto an instrument). For the purpose of this document, a computer is atechnical computer which in particular comprises technical, inparticular tangible components, in particular mechanical and/orelectronic components. Any device mentioned as such in this document isa technical and in particular tangible device.

Analytical devices such as x-ray devices, CT devices or MRT devices areused to generate analytical images (such as x-ray images or MRT images)of the body. Analytical devices use imaging methods, in particularso-called “medical imaging methods”, for analysing a patient's body, forinstance by using waves and/or radiation and/or energy beams, inparticular electromagnetic waves and/or radiation, ultrasound wavesand/or particles beams. Analytical devices are in particular deviceswhich generate images (for example, two-dimensional or three-dimensionalimages) of the patient's body (and in particular of internal structuresand/or anatomical parts of the patient's body) by analysing the body.The images are also referred to as “medical images”. Analytical devicesare in particular used in medical diagnosis, in particular in radiology.

In the field of medicine, imaging methods (also called medical imagingmethods and/or imaging modalities and/or medical imaging modalities) areused to generate image data (for example, two-dimensional orthree-dimensional image data) of anatomical structures (such as softtissues, bones, organs, etc.) of the human body. The term “medicalimaging methods” is understood to mean (advantageously apparatus-based,in particular analytical device-based) imaging methods (so-calledmedical imaging modalities and/or radiological imaging methods) such asfor instance computed tomography (CT) and cone beam computed tomography(CBCT, in particular volumetric CBCT), x-ray tomography, magneticresonance tomography (MRT or MRI), conventional x-ray, sonography and/orultrasound examinations, and positron emission tomography. Analyticaldevices in particular are used to generate the image data inapparatus-based imaging methods. The image data describe images whichare also referred to as medical images. The imaging methods are inparticular used for medical diagnostics, to analyse the anatomical bodyin order to generate images which are described by the image data. Theimaging methods are also in particular used to detect pathologicalchanges in the human body.

The method in accordance with the invention is in particular a dataprocessing method. The data processing method is preferably performedusing technical means, in particular a computer. The data processingmethod is preferably constituted to be executed by or on a computer andin particular is executed by or on the computer. In particular, all thesteps or merely some of the steps (i.e. less than the total number ofsteps) of the method in accordance with the invention can be executed bya computer. The computer in particular comprises a processor and amemory in order to process the data, in particular electronically and/oroptically. The calculating steps described are in particular performedby a computer. Determining steps or calculating steps are in particularsteps of determining data within the framework of the technical dataprocessing method, in particular within the framework of a program. Acomputer is in particular any kind of data processing device, inparticular electronic data processing device. A computer can be a devicewhich is generally thought of as such, for example desktop PCs,notebooks, netbooks, etc., but can also be any programmable apparatus,such as for example a mobile phone or an embedded processor. A computercan in particular comprise a system (network) of “sub-computers”,wherein each sub-computer represents a computer in its own right. Theterm “computer” includes a cloud computer, in particular a cloud server.The term “cloud computer” includes a cloud computer system which inparticular comprises a system of at least one cloud computer and inparticular a plurality of operatively interconnected cloud computerssuch as a server farm. Such a cloud computer is preferably connected toa wide area network such as the world wide web (WWW) and located in aso-called cloud of computers which are all connected to the world wideweb. Such an infrastructure is used for “cloud computing”, whichdescribes computation, software, data access and storage services whichdo not require the end user to know the physical location and/orconfiguration of the computer delivering a specific service. Inparticular, the term “cloud” is used in this respect as a metaphor forthe Internet (world wide web). In particular, the cloud providescomputing infrastructure as a service (IaaS). The cloud computer canfunction as a virtual host for an operating system and/or dataprocessing application which is used to execute the method of theinvention. The cloud computer is for example an elastic compute cloud(EC2) as provided by Amazon Web Services™. A computer in particularcomprises interfaces in order to receive or output data and/or performan analogue-to-digital conversion. The data are in particular data whichrepresent physical properties and/or which are generated from technicalsignals. The technical signals are in particular generated by means of(technical) detection devices (such as for example devices for detectingmarker devices) and/or (technical) analytical devices (such as forexample devices for performing imaging methods), wherein the technicalsignals are in particular electrical or optical signals. The technicalsignals in particular represent the data received or outputted by thecomputer. The computer is preferably operatively coupled to a displaydevice which allows information outputted by the computer to bedisplayed, for example to a user. One example of a display device is anaugmented reality device (also referred to as augmented reality glasses)which can be used as “goggles” for navigating. A specific example ofsuch augmented reality glasses is Google Glass (a trademark of Google,Inc.). An augmented reality device can be used both to input informationinto the computer by user interaction and to display informationoutputted by the computer.

The expression “acquiring data” in particular encompasses (within theframework of a data processing method) the scenario in which the dataare determined by the data processing method or program. Determiningdata in particular encompasses measuring physical quantities andtransforming the measured values into data, in particular digital data,and/or computing the data by means of a computer and in particularwithin the framework of the method in accordance with the invention. Themeaning of “acquiring data” also in particular encompasses the scenarioin which the data are received or retrieved by the data processingmethod or program, for example from another program, a previous methodstep or a data storage medium, in particular for further processing bythe data processing method or program. The expression “acquiring data”can therefore also for example mean waiting to receive data and/orreceiving the data. The received data can for example be inputted via aninterface. The expression “acquiring data” can also mean that the dataprocessing method or program performs steps in order to (actively)receive or retrieve the data from a data source, for instance a datastorage medium (such as for example a ROM, RAM, database, hard drive,etc.), or via the interface (for instance, from another computer or anetwork). The data can be made “ready for use” by performing anadditional step before the acquiring step. In accordance with thisadditional step, the data are generated in order to be acquired. Thedata are in particular detected or captured (for example by ananalytical device). Alternatively or additionally, the data are inputtedin accordance with the additional step, for instance via interfaces. Thedata generated can in particular be inputted (for instance into thecomputer). In accordance with the additional step (which precedes theacquiring step), the data can also be provided by performing theadditional step of storing the data in a data storage medium (such asfor example a ROM, RAM, CD and/or hard drive), such that they are readyfor use within the framework of the method or program in accordance withthe invention. The step of “acquiring data” can therefore also involvecommanding a device to obtain and/or provide the data to be acquired. Inparticular, the acquiring step does not involve an invasive step whichwould represent a substantial physical interference with the body,requiring professional medical expertise to be carried out and entailinga substantial health risk even when carried out with the requiredprofessional care and expertise. In particular, the step of acquiringdata, in particular determining data, does not involve a surgical stepand in particular does not involve a step of treating a human or animalbody using surgery or therapy. In order to distinguish the differentdata used by the present method, the data are denoted (i.e. referred to)as “XY data” and the like and are defined in terms of the informationwhich they describe, which is then preferably referred to as “XYinformation” and the like.

Image fusion can be elastic image fusion or rigid image fusion. In thecase of rigid image fusion, the relative position between the pixels ofa 2D image and/or voxels of a 3D image is fixed, while in the case ofelastic image fusion, the relative positions are allowed to change.

Elastic fusion transformations (for example, elastic image fusiontransformations) are in particular designed to enable a seamlesstransition from one data set (for example a first data set such as forexample a first image) to another data set (for example a second dataset such as for example a second image). The transformation is inparticular designed such that one of the first and second data sets(images) is deformed, in particular in such a way that correspondingstructures (in particular, corresponding image elements) are arranged atthe same position as in the other of the first and second images. Thedeformed (transformed) image which is transformed from one of the firstand second images is in particular as similar as possible to the otherof the first and second images. Preferably, (numerical) optimisationalgorithms are applied in order to find the transformation which resultsin an optimum degree of similarity. The degree of similarity ispreferably measured by way of a measure of similarity (also referred toin the following as a “similarity measure”). The parameters of theoptimisation algorithm are in particular vectors of a deformation field.These vectors are determined by the optimisation algorithm in such a wayas to result in an optimum degree of similarity. Thus, the optimumdegree of similarity represents a condition, in particular a constraint,for the optimisation algorithm. The bases of the vectors lie inparticular at voxel positions of one of the first and second imageswhich is to be transformed, and the tips of the vectors lie at thecorresponding voxel positions in the transformed image. A plurality ofthese vectors are preferably provided, for instance more than twenty ora hundred or a thousand or ten thousand, etc. Preferably, there are(other) constraints on the transformation (deformation), in particularin order to avoid pathological deformations (for instance, all thevoxels being shifted to the same position by the transformation). Theseconstraints include in particular the constraint that the transformationis regular, which in particular means that a Jacobian determinantcalculated from a matrix of the deformation field (in particular, thevector field) is larger than zero, and also the constraint that thetransformed (deformed) image is not self-intersecting and in particularthat the transformed (deformed) image does not comprise faults and/orruptures. The constraints include in particular the constraint that if aregular grid is transformed simultaneously with the image and in acorresponding manner, the grid is not allowed to interfold at any of itslocations. The optimising problem is in particular solved iteratively,in particular by means of an optimisation algorithm which is inparticular a first-order optimisation algorithm, in particular agradient descent algorithm. Other examples of optimisation algorithmsinclude optimisation algorithms which do not use derivations, such asthe downhill simplex algorithm, or algorithms which use higher-orderderivatives such as Newton-like algorithms. The optimisation algorithmpreferably performs a local optimisation. If there is a plurality oflocal optima, global algorithms such as simulated annealing or genericalgorithms can be used. In the case of linear optimisation problems, thesimplex method can for instance be used.

In the steps of the optimisation algorithms, the voxels are inparticular shifted by a magnitude in a direction such that the degree ofsimilarity is increased. This magnitude is preferably less than apredefined limit, for instance less than 1/10 or 1/100 or 1/1000 of thediameter of the image, and in particular about equal to or less than thedistance between neighbouring voxels. Large deformations can beimplemented, in particular due to a high number of (iteration) steps.

The determined elastic fusion transformation can in particular be usedto determine a degree of similarity (or similarity measure, see above)between the first and second data sets (first and second images). Tothis end, the deviation between the elastic fusion transformation and anidentity transformation is determined. The degree of deviation can forinstance be calculated by determining the difference between thedeterminant of the elastic fusion transformation and the identitytransformation. The higher the deviation, the lower the similarity,hence the degree of deviation can be used to determine a measure ofsimilarity.

A measure of similarity can in particular be determined on the basis ofa determined correlation between the first and second data sets. It ispossible to decide whether or not there is similarity by means of such asimilarity measure (see Definitions) and in particular by means of apredetermined (percentage) threshold. The term “similar” as used hereencompasses the term “identical”. Similarity may for example be assumedif the deviation from identity is less than 30%, in particular less than20% and preferably less than 10%.

The method in accordance with the invention is preferably at leastpartly executed by a computer, i.e. all the steps or merely some of thesteps (i.e. less than the total number of steps) of the method inaccordance with the invention can be executed by a computer.

1. A medical system for determining metastases in an image of ananatomical body part, comprising: an analytical device for generatingimages of an anatomical body part; at least one computer operable toreceive the images from the analytical device and having at least oneprocessor and memory with instructions, the analytical device operableto receive an enhanced image of the anatomical body part using acontrasting agent and receive a non-enhanced image of the anatomicalbody part without using the contrasting agent, the instructions, whenexecuted on the computer, configuring the computer to: spatiallycorrelate the enhanced image and the non-enhanced image to create acommon reference system; wherein both the enhanced image and thenon-enhanced image include two dimensional layers of voxels; transformat least one of the enhanced image and the non-enhanced image to createa bijective assignment between the voxels and the respective image;create a normalized difference image and acquire a non-tumor region inboth the enhanced image and the non-enhanced image; wherein each of thevoxels of the enhanced image and the voxels of the non-enhanced imagehave voxel intensities and wherein the normalized difference image has aplurality of voxels each with an associated voxel intensity; acquire anintensity threshold of the normalized difference image to differentiatebetween the voxel intensities caused by the contrast agent and the voxelintensities not caused by the contrast agent; using the intensitythreshold to determine a three-dimensional binary image, acquiremetastasis geometry data which describe geometrical properties of ametastasis in the binary image; determine a set of adjacent imageelements; analyze the 2D properties of the set; and determine that theset represents a metastasis if the set exhibits two-dimensionalgeometrical properties which comply with the geometrical propertiesdescribed by the metastasis geometry data.
 2. The system of claim 1 thewherein both the enhanced image and the non-enhanced image include thetwo dimensional layers of voxels positioned one above the other.
 3. Thesystem of claim 1 wherein the bijective assignment between the voxelsand the respective image are created so that a 1:1 correspondencebetween the voxels in the respective image is established.
 4. The systemof claim 1 wherein the binary image comprises a plurality of imagelayers which comprise a plurality of pixels.
 5. The system of claim 4wherein the binary image is a three-dimensional image and comprises aplurality of image layers which comprise a plurality of pixels.
 6. Thesystem of claim 1 the set of adjacent image elements represent theenhanced signal intensity and are integrally closed and apart from othersets and/or image elements which represent the enhanced signalintensity.
 7. A computer implemented method for determining metastaseswithin an anatomical body part, wherein the metastases exhibits anenhanced signal in an image of the anatomical body part generated by amedical imaging method using a contrast agent, the method being designedto be performed by a computer and comprising the following steps:acquiring an enhanced image of the anatomical body part obtained by amedical imaging method using the contrast agent; acquiring anon-enhanced image of the anatomical body part obtained by a medicalimaging method without using the contrast agent; spatially correlatingthe enhanced image and the non-enhanced image with respect to eachother; acquiring non-enhanced sub-regions in each of the enhanced imageand the non-enhanced image, wherein the non-enhanced sub-regions areassumed or known to not exhibit an enhanced signal; determining anormalized difference image by normalizing the spatially correlatedenhanced and non-enhanced images with respect to each other on the basisof the intensities in the respective non-enhanced sub-regions and byperforming a difference operation on the normalized spatially correlatedenhanced and non-enhanced images; acquiring an intensity condition fordifferentiating between the structure of interest and other structureswhich do not exhibit an enhanced signal; determining a binary image onthe basis of the intensity condition and the normalized differenceimage, wherein the binary image comprises image elements representingeither an enhanced signal intensity or a non-enhanced signal intensity;wherein the binary image is a three-dimensional image and comprises aplurality of image layers which comprise a plurality of pixels and themethod further comprising the steps of: acquiring metastasis geometrydata which describe geometrical properties of a metastasis in the imagelayers; determining a set of adjacent image elements which represent theenhanced signal intensity and are integrally closed and apart from othersets and/or image elements which represent the enhanced signalintensity; analyzing the 2D properties of the set; and determining thatthe set represents a metastasis if the set exhibits two-dimensionalgeometrical properties which comply with the geometrical propertiesdescribed by the metastasis geometry data.
 8. The method according toclaim 7, wherein the geometrical properties include relative geometricalproperties, which describe the relative variation in the geometry of themetastasis of one image layer with respect to another image layer. 9.The method according to claim 8, wherein the another image layer is anadjacent image layer.
 10. The method according to claim 7, wherein theset is determined to represent a metastasis if the analyzed 2Dproperties of the set exhibit two-dimensional geometrical propertieswhich match the geometrical properties described by the metastasisgeometry data.
 11. The method according to claim 10, wherein thegeometrical property is that the borderline of the set has a shapesimilar to a circle.
 12. The method according to claim 11, wherein theset is identified to represent a metastasis if combined slices of theset are similar to a spherical shape.
 13. The method according to claim7 wherein the set is identified to represent a metastasis if slices ofthe set are arranged concentrically.
 14. The method according to claim7, wherein a two-dimensional layer of image elements from the set isreferred to as a metastasis slice, and wherein the metastasis geometrydata describe a metastasis as exhibiting at least one of the followingfeatures: the metastasis slices are in adjacent image layers; a shape ofthe metastasis slices arranged one above the other in adjacent imagelayers is similar; the size of a metastasis slice between two othermetastasis slices is larger than the size of at least one of the twoother metastasis slices.
 15. A method of enhancing a structure ofinterest within an anatomical body part in an image of the body part,comprising: acquiring an enhanced image of the anatomical body partobtained by medical imaging using a contrasting agent: acquiring anon-enhanced image of the anatomical body part obtained by medicalimaging without using the contrasting agent; spatially correlating theenhanced image and the non-enhanced image to create a common referencesystem; wherein both the enhanced image and the non-enhanced imageinclude two dimensional layers of voxels positioned one above the other;transforming at least one of the enhanced image and the non-enhancedimage to create a bijective assignment between the voxels and therespective image so that a 1:1 correspondence between the voxels in therespective image is established; creating a normalized difference imageand acquiring a non-tumor region in both the enhanced image and thenon-enhanced image; wherein each of the voxels of the enhanced image andthe voxels of the non-enhanced image have voxel intensities and whereinthe normalized difference image has a plurality of voxels each with anassociated voxel intensity; acquiring an intensity threshold of thenormalized difference image to differentiate between the voxelintensities caused by the contrast agent and the voxel intensities notcaused by the contrast agent, wherein the binary image is athree-dimensional image and comprises a plurality of image layers whichcomprise a plurality of pixels and the method further comprising thesteps of: acquiring metastasis geometry data which describe geometricalproperties of a metastasis in the image layers; determining a set ofadjacent image elements which represent the enhanced signal intensityand are integrally closed and apart from other sets and/or imageelements which represent the enhanced signal intensity; analyzing the 2Dproperties of the set; and determining that the set represents ametastasis if the set exhibits two-dimensional geometrical propertieswhich comply with the geometrical properties described by the metastasisgeometry data.